AMAZON REDSHIFT

AMAZON REDSHIFT

The growth of an organization also means an exponential growth of data that needs to be stored, monitored and analyzed. Organizations that run on traditional database warehouses will face difficulties- because queries start taking more time, which makes data difficult to manage.

Cloud Computing has enabled the growth of such warehousing solutions that require scaling and coping with the increasing demands of data storage and analysis, resulting in organizations looking for alternatives to traditional on-premise warehousing.

In this blog, we have a look at Amazon Redshift, a direct response to this demand for data warehousing- and how it can benefit your organization!


So, What is Amazon Redshift?  

Amazon Redshift is a fully-managed petabyte-scale cloud based data warehouse product designed for large scale data set storage and analysis. It is also used to perform large scale database migrations.

Redshift is designed to be fully compatible with SQL-based workloads, and BI (business Intelligence) tools, which makes data available to users in real time. Redshift delivers fast performance and efficient querying that help teams make sound business analyses and decisions.

Redshift delivers incredibly fast performance using two key architectural elements: columnar data storage and massively parallel processing design. The solution has quickly become an integral part of the big data analytics landscape through its ability to perform SQL-based queries on large databases containing a mix of structured, unstructured, and unstructured data.

Some may confuse Amazon S3 and AWS Redshift. While both are Amazon Web Service products, S3 is used specifically for product storage, and AWS Redshift is distinctly a data warehouse.


Amazon Redshift vs Traditional Data Warehouses  

To begin with, let's understand what a Data Warehouse is. A Data Warehouse is a type of Data Management system, which is designed to support Business Intelligence activities, especially those to do with Data Analytics.

Traditionally, enterprises would set up and invest in their own on-premise data warehouses.The challenges that come with it range from being expensive to manage and service, to taking months to get it up and running, not overlooking the extra manpower required to maintain this infrastructure. This factor requires firm budgetary and strategic commitment from leadership.

To add to this, after a few months or years, data size invariably tends to increase, meaning companies needed to choose between investing in new hardware or tolerating slow performance.

Redshift’s cloud-based solution helps organisations overcome these issues. It takes just minutes to create a cluster from the AWS console. Data ingestion into Redshift is performed by issuing a simple COPY command from Amazon S3, or DynamoDB. Additionally, the scalable architecture of Redshift allows companies to place a dynamic request to scale infrastructure according to their requirements.

Being a fully managed AWS service, Redshift eliminates the hassle of routine database administration tasks. Complex tasks such as data encryption are also deployed easily through Redshift’s built-in security features. Data is continuously backed as well, eliminating the risk of losing data.

Given the cost-effective, reliable, scalable, and fast performing solution that redshift is, it is clearly a service that brings more benefit to organisations as compared to a traditional data warehouse.


Features of Amazon Redshift

1. Column-oriented databases: Data can be organized either into rows or columns. What determines the type of organization is the nature of the workload. Column-oriented databases allow for increased speed when it comes to accessing large amounts of data. For example, in an online analytical processing—or OLAP—environment such as Redshift, users generally apply a smaller number of queries to much larger datasets. In this scenario, being a column-oriented database allows Redshift to complete massive data processing jobs quickly.

2. Massively parallel processing (MPP): MPP is a distributed design approach in which several processors apply a "divide and conquer" strategy to large data jobs. A large processing job is organized into smaller jobs which are then distributed among a cluster of processors (compute nodes). The processors complete their computations simultaneously rather than sequentially. The result is a large reduction in the amount of time Redshift needs to complete a single, massive job.

3. End-to-end data encryption: Encryption options in Redshift are robust and highly customizable. This flexibility allows users to configure an encryption standard that best fits their needs. Redshift security encryption features include:

- The option of employing either an AWS-managed or a customer-managed key

- Migrating data between encrypted and unencrypted clusters

- A choice between AWS Key Management Service or HSM (hardware security module)

- Options to apply single or double encryption, depending on the scenario

4. Network isolation: For businesses that want additional security, administrators can choose to isolate their network within Redshift. In this scenario, network access to an organization's cluster is restricted by enabling the Amazon VPC. The user's data warehouse remains connected to the existing IT infrastructure with IPsec VPN.

5. Fault tolerance: Fault tolerance refers to the ability of a system to continue functioning even when some components fail. When it comes to data warehousing, fault tolerance determines the capacity for a job to continue being run when some processors or clusters are offline. When drives, nodes, or clusters fail, Redshift automatically re-replicates data and shifts data to healthy nodes.

6. Concurrency limits: Concurrency limits determine the maximum number of nodes or clusters that a user can provision at any given time. These limits ensure that adequate compute resources are available to all users. In this sense, concurrency limits democratize the data warehouse. Redshift configures limits based on regions, rather than applying a single limit to all users. In some situations, users may submit a limit increase request.


Use Cases

1. OpenPayGroup: a fast-growing player in the global BNPL payment solutions market. The company’s robust platform enables it to deliver flexible plans in the market with loan durations of 2–24 months and loan amounts of up to $20,000. OpenPay built a data warehouse using Amazon Redshift to store data lake information in a structured format. The transition to redshift also helped them carry out quick and efficient reporting, which helped with individual productivity as well.

2. BankBazaar: The business issues co-branded credit cards and provides services such as free credit score checks and personal financial management tools for about 50 million users across the country. As a company reliant on data science,  Amazon Redshift is the cornerstone of the business’s data analytics strategy and supports the analysis of customer events, behaviour, and information to deliver services such as financial product recommendations based on credit policies and applicants’ creditworthiness.

3. SiCepat Ekspres: They are one of the largest last-mile delivery companies in Indonesia. Their partners include major e-commerce businesses such as Tokopedia. SiCepat Ekspres uses Amazon Redshift and Amazon Athena to process and query large amounts of data to generate operational metrics and key data insights to support their fast growing business. This has given them the scalability and cost optimization they needed, plus improved operational efficiency with no incidents and no downtime. During peak seasons, they can scale their delivery capacity by up to 300 percent in just minutes.


How can Ataloud help?

Connect with an Ataloud consultant today (yohan@ataloud.com) for a seamless experience for your business' transition to the cloud. We can analyse, discuss and help validate your AWS billing and usage patterns, perform routine audits, perform log analysis, analyse and monitor performances- on top of the other managed services that we offer. 

I am looking for recommendations for professionals who can help me with Data Entry job, i can perform manual data processing to extract it from PDF documents into Word files. I need job please help me 🙏

Like
Reply

To view or add a comment, sign in

More articles by Ataloud

Insights from the community

Others also viewed

Explore topics